OHI British Columbia | OHI Science | Citation policy
knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'figs/',
echo = TRUE, message = FALSE, warning = FALSE)
library(raster)
library(data.table)
source('https://raw.githubusercontent.com/oharac/src/master/R/common.R')
### includes library(tidyverse); library(stringr);
### dir_M points to ohi directory on Mazu; dir_O points to home dir on Mazu
dir_git <- '~/github/spp_risk_dists'
### goal specific folders and info
dir_setup <- file.path(dir_git, 'data_setup')
dir_data <- file.path(dir_git, 'data')
dir_o_anx <- file.path(dir_O, 'git-annex/spp_risk_dists')
source(file.path(dir_git, 'data_setup/api_fxns.R'))Compare MPAs to biodiversity intactness:
shp_to_taxa <- read_csv(file.path(dir_git, 'data_setup/raw', 'shps_to_taxa.csv'))
taxa_sums <- data.frame(unweighted = list.files(file.path(dir_o_anx, 'taxa_summaries'),
pattern = sprintf('cell_sum_%s.csv', api_version),
full.names = TRUE),
rr_weighted = list.files(file.path(dir_o_anx, 'taxa_summaries'),
pattern = sprintf('cell_sum_rrweight_%s.csv', api_version),
full.names = TRUE)) %>%
mutate(shp_basename = str_replace_all(basename(unweighted), '_cell_sum.+|_part.+', '')) %>%
left_join(shp_to_taxa, by = 'shp_basename')
# spp_taxonomy <- read_csv(file.path(dir_o_anx, sprintf('iucn/spp_info_from_api_%s.csv', api_version))) %>%
# select(iucn_sid, sciname, kingdom, phylum, class)
#
# spp_info <- read_csv(file.path(dir_git, 'data', sprintf('spp_marine_maps_%s.csv', api_version)),
# col_types = 'ddciccc') %>%
# select(iucn_sid, sciname, dbf_file) %>%
# left_join(spp_taxonomy %>% select(-sciname), by = 'iucn_sid')
# phyla_summaries <- spp_info %>%
# mutate(phylum = tolower(phylum), class = tolower(class)) %>%
# group_by(phylum) %>%
# mutate(n_phylum = n()) %>%
# group_by(phylum, class, n_phylum) %>%
# summarize(n_class = n()) %>%
# ungroup() %>%
# mutate(taxa_gp = ifelse(phylum == 'chordata',
# paste0('Chordata: ', tools::toTitleCase(class)),
# tools::toTitleCase(phylum)),
# n_gp = ifelse(phylum == 'chordata', n_class, n_phylum)) %>%
# select(taxa_gp, n_gp) %>%
# distinct()
# spp_info_all_marine <- read_csv(file.path(dir_git, 'data',
# sprintf('spp_marine_from_api_%s.csv', api_version))) %>%
# left_join(spp_taxonomy, by = 'iucn_sid')
#
# spp_info_all_marine$class %>% unique()
# [1] "ACTINOPTERYGII" "REPTILIA" "MAMMALIA" "CHONDRICHTHYES"
# [5] "GASTROPODA" "MAXILLOPODA" "ENOPLA" "CEPHALASPIDOMORPHI"
# [9] "SARCOPTERYGII" "MEROSTOMATA" "AMPHIBIA" "INSECTA"
# [13] "MAGNOLIOPSIDA" "PINOPSIDA" "LILIOPSIDA" "CYCADOPSIDA"
# [17] "FLORIDEOPHYCEAE" "ANTHOZOA" "ULVOPHYCEAE" "PHAEOPHYCEAE"
# [21] "HYDROZOA" "MALACOSTRACA" "MYXINI" "BIVALVIA"
# [25] "CEPHALOPODA" "POLYPODIOPSIDA" "HOLOTHUROIDEA" "AVES"
# [29] "LYCOPODIOPSIDA" "CLITELLATA" spp_info <- read_csv(file.path(dir_git, 'data', sprintf('spp_marine_maps_%s.csv', api_version)),
col_types = 'ddciccc') %>%
mutate(shp_basename = str_replace_all(basename(tolower(dbf_file)), '.dbf|_part.+', '')) %>%
select(iucn_sid, sciname, shp_basename) %>%
left_join(taxa_sums, by = 'shp_basename')
taxa_gps <- spp_info$taxon %>% unique()
reload <- FALSE
for(taxon_gp in taxa_gps) { ### taxon_gp <- taxa_gps[1]
taxon_txt <- taxon_gp %>% tolower() %>% str_replace_all('chordata: |[^a-z]+', '')
dens_plot_file <- file.path(dir_git, 'ms_figures/taxa_figs',
sprintf('fig_SI_density_risk_%s.png',
taxon_txt))
threat_plot_file <- file.path(dir_git, 'ms_figures/taxa_figs',
sprintf('fig_SI_pct_threat_%s.png',
taxon_txt))
if(any(!file.exists(c(dens_plot_file, threat_plot_file))) | reload == TRUE) {
taxa_info <- spp_info %>%
filter(taxon == taxon_gp)
n_spp_in_taxa <- unique(taxa_info$iucn_sid) %>% length()
cat(paste0('\n\n#### ', taxon_gp, ' (', n_spp_in_taxa, ' species)\n\n'))
message('Processing ', taxon_gp)
unweighted_files <- taxa_info$unweighted %>% unique()
rr_weighted_files <- taxa_info$rr_weighted %>% unique()
taxon_df <- parallel::mclapply(unweighted_files, mc.cores = 12,
FUN = function (x) { ### x <- unweighted_files[1]
read_csv(x, col_types = 'dddiidi')
}) %>%
bind_rows() %>%
group_by(cell_id) %>%
summarize(mean_risk_sum = 1/sum(n_spp_risk) * sum(mean_risk * n_spp_risk),
pct_threat_sum = sum(n_spp_threatened) / sum(n_spp_risk),
n_spp_sum = sum(n_spp_risk)) %>%
mutate(wt = 'unweighted')
taxon_df_rr <- parallel::mclapply(rr_weighted_files, mc.cores = 12,
FUN = function (x) { ### x <- rr_weighted_files[1]
read_csv(x, col_types = 'ddd___d___')
}) %>%
bind_rows() %>%
group_by(cell_id) %>%
summarize(mean_risk_sum = 1/sum(sr_rr_risk) * sum(mean_risk * sr_rr_risk),
pct_threat_sum = sum(sr_rr_threatened) / sum(sr_rr_risk)) %>%
select(cell_id, mean_risk_sum, pct_threat_sum) %>%
mutate(wt = 'rr_weighted')
df <- taxon_df %>%
bind_rows(taxon_df_rr)
x <- ggplot(df, aes(x = mean_risk_sum, fill = wt, color = wt, ..scaled..)) +
ggtheme_plot() +
geom_density(alpha = .3, size = .25, bw = .015) +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank()) +
xlim(0, 1) +
labs(title = paste0(taxon_gp, ': risk'))
ggsave(filename = dens_plot_file,
width = 6, height = 4, units = 'in', dpi = 300)
y <- ggplot(df, aes(x = pct_threat_sum, fill = wt, color = wt, ..scaled..)) +
ggtheme_plot() +
geom_density(alpha = .3, size = .25, bw = .015) +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank()) +
xlim(0, 1) +
labs(title = paste0(taxon_gp, ': pct threatened'))
ggsave(filename = threat_plot_file,
width = 6, height = 4, units = 'in', dpi = 300)
}
cat(sprintf('\n\n', path.expand(dens_plot_file)))
cat(sprintf('\n\n', path.expand(threat_plot_file)))
}reload <- FALSE
rast_base <- raster(ext = extent(c(-180, 180, -90, 90)), res = 0.1)
values(rast_base) <- 1:length(rast_base)
land_poly <- sf::read_sf(file.path(dir_git, 'spatial/ne_10m_land/ne_10m_land.shp'))
taxa_gps <- spp_info$taxon %>% unique()
for(taxon_gp in taxa_gps) { ### taxon_gp <- taxa_gps[1]
taxon_txt <- taxon_gp %>% tolower() %>% str_replace_all('chordata: |[^a-z]+', '')
map_plot_file <- file.path(dir_git, 'ms_figures/taxa_maps',
sprintf('fig_SI_map_risk_%s.png',
taxon_txt))
rr_map_plot_file <- file.path(dir_git, 'ms_figures/taxa_maps',
sprintf('fig_SI_map_rr_risk_%s.png',
taxon_txt))
### Check whether plots already exist.
if(any(!file.exists(c(map_plot_file, rr_map_plot_file))) | reload == TRUE) {
taxa_info <- spp_info %>%
filter(taxon == taxon_gp)
n_spp_in_taxa <- unique(taxa_info$iucn_sid) %>% length()
cat(paste0('\n\n#### ', taxon_gp, ' (', n_spp_in_taxa, ' species)\n\n'))
message('Processing ', taxon_gp)
unweighted_files <- taxa_info$unweighted %>% unique()
rr_weighted_files <- taxa_info$rr_weighted %>% unique()
### process data for unweighted maps
taxon_df <- parallel::mclapply(unweighted_files, mc.cores = 12,
FUN = function (x) { ### x <- unweighted_files[1]
read_csv(x, col_types = 'dddiidi')
}) %>%
bind_rows() %>%
group_by(cell_id) %>%
summarize(mean_risk_sum = 1/sum(n_spp_risk) * sum(mean_risk * n_spp_risk),
pct_threat_sum = sum(n_spp_threatened) / sum(n_spp_risk),
n_spp_sum = sum(n_spp_risk)) %>%
mutate(wt = 'unweighted')
### create raster of unweighted map, then rasterToPoints it. Could also
### do a cell ID to lat/long, but whatevs.
mean_rast <- subs(rast_base, taxon_df, by = 'cell_id', which = 'mean_risk_sum')
mean_df <- mean_rast %>%
# aggregate(fact = 4) %>%
rasterToPoints() %>%
as.data.frame() %>%
setNames(c('long', 'lat', 'value'))
message('ggplotting map for ', taxon_gp)
x <- ggplot(mean_df) +
geom_raster(aes(long, lat, fill = value)) +
geom_sf(data = land_poly, aes(geometry = geometry), fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map() +
theme(panel.background = element_rect(fill = 'grey85')) +
coord_sf(datum = NA) +
scale_fill_gradientn(colors = c('green4', 'lightyellow', 'red2', 'red3', 'red4', 'purple4'),
limits = c(0, 1),
labels = c('LC', 'NT', 'VU', 'EN', 'CR', 'EX'),
breaks = c( 0.0, 0.2, 0.4, 0.6, 0.8, 1.0)) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(title = paste0('Mean risk: ', taxon_gp),
fill = 'Mean risk')
ggsave(filename = map_plot_file, width = 6, height = 4, units = 'in', dpi = 300)
message('now ggplotting the range-rarity map...')
taxon_df_rr <- parallel::mclapply(rr_weighted_files, mc.cores = 12,
FUN = function (x) { ### x <- rr_weighted_files[1]
read_csv(x, col_types = 'ddd___d___')
}) %>%
bind_rows() %>%
group_by(cell_id) %>%
summarize(mean_risk_sum = 1/sum(sr_rr_risk) * sum(mean_risk * sr_rr_risk),
pct_threat_sum = sum(sr_rr_threatened) / sum(sr_rr_risk)) %>%
select(cell_id, mean_risk_sum, pct_threat_sum) %>%
mutate(wt = 'rr_weighted')
mean_rr_rast <- subs(rast_base, taxon_df_rr, by = 'cell_id', which = 'mean_risk_sum')
mean_rr_df <- mean_rr_rast %>%
# aggregate(fact = 4) %>%
rasterToPoints() %>%
as.data.frame() %>%
setNames(c('long', 'lat', 'value'))
x <- ggplot(mean_rr_df) +
geom_raster(aes(long, lat, fill = value)) +
geom_sf(data = land_poly, aes(geometry = geometry), fill = 'grey96', color = 'grey40', size = .10) +
ggtheme_map() +
theme(panel.background = element_rect(fill = 'grey85')) +
coord_sf(datum = NA) +
scale_fill_gradientn(colors = c('green4', 'lightyellow', 'red2', 'red3', 'red4', 'purple4'),
limits = c(0, 1),
labels = c('LC', 'NT', 'VU', 'EN', 'CR', 'EX'),
breaks = c( 0.0, 0.2, 0.4, 0.6, 0.8, 1.0)) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(title = paste0('RR-weighted mean risk: ', taxon_gp),
fill = 'Mean risk')
ggsave(filename = rr_map_plot_file,
width = 6, height = 4, units = 'in', dpi = 300)
} else {
message('Maps exist: \n ', map_plot_file, '\n ', rr_map_plot_file)
}
cat(sprintf('\n\n', path.expand(map_plot_file)))
cat(sprintf('\n\n', path.expand(rr_map_plot_file)))
}spp_ranges <- read_csv(file.path(dir_data, sprintf('iucn_spp_range_area_%s.csv', api_version)),
col_types = 'dd__') %>%
distinct()
taxa_ranges <- read_csv(file.path(dir_git, 'data', sprintf('spp_marine_maps_%s.csv', api_version)),
col_types = 'ddciccc') %>%
mutate(shp_basename = str_replace_all(basename(tolower(dbf_file)), '.dbf|_part.+', '')) %>%
left_join(spp_ranges, by = 'iucn_sid') %>%
left_join(shp_to_taxa, by = 'shp_basename') %>%
mutate(log_range = log10(range_km2))
ggplot(taxa_ranges, aes(x = log_range, ..scaled..)) +
ggtheme_plot() +
theme(panel.grid.major = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
strip.text.y = element_text(angle = 0, hjust = 0)) +
geom_density(color = 'grey20', size = .25, fill = 'grey30') +
scale_x_continuous(limits = c(0, NA),
breaks = c(0:8),
labels = 10^(0:8)) +
labs(x = 'Range, km^2') +
facet_grid(taxon ~ .)ggsave(filename = file.path(dir_git, 'ms_figures', 'fig_SI_taxa_ranges.png'),
width = 6, height = 4, units = 'in', dpi = 300)